"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_A        =     {
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A        =     ['LayoutLMv2TokenizerFast']

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A        =     ['LayoutLMv2FeatureExtractor']
						_A        =     ['LayoutLMv2ImageProcessor']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A        =     [
						    'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'LayoutLMv2ForQuestionAnswering',
						    'LayoutLMv2ForSequenceClassification',
						    'LayoutLMv2ForTokenClassification',
						    'LayoutLMv2Layer',
						    'LayoutLMv2Model',
						    'LayoutLMv2PreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
						from .processing_layoutlmva import LayoutLMvaProcessor
						from .tokenization_layoutlmva import LayoutLMvaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_layoutlmva import (
												    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LayoutLMvaForQuestionAnswering,
												    LayoutLMvaForSequenceClassification,
												    LayoutLMvaForTokenClassification,
												    LayoutLMvaLayer,
												    LayoutLMvaModel,
												    LayoutLMvaPreTrainedModel,
												)
else:
						import sys

						_A        =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








"""simple docstring"""



def      UpperCAmelCase     (       _lowercase :   Optional[Any]	)						-> Tuple:


	"""simple docstring"""




	lowerCAmelCase_      							=			len(_lowercase	)
	lowerCAmelCase_      							=			sum(_lowercase	)

	lowerCAmelCase_      							=			[[False for x in range(s + 1	)] for y in range(n + 1	)]

	for i in range(1			,   n + 1	):
		lowerCAmelCase_      							=			True

	for i in range(1			,   s + 1	):
		lowerCAmelCase_      							=			False

	for i in range(1			,   n + 1	):
		for j in range(1			,   s + 1	):
			lowerCAmelCase_      							=			dp[i][j - 1]

			if arr[i - 1] <= j:
				lowerCAmelCase_      							=			dp[i][j] or dp[i - 1][j - arr[i - 1]]

	for j in range(int(s / 2	)			,   -1			,   -1	):
		if dp[n][j] is True:
			lowerCAmelCase_      							=			s - 2 * j
			break

	return diff